DE
Zoe Dellaert
2024-10-05
Differential expression analysis of LCM RNA Data
Managing Packages Using Renv
To run this code in my project using the renv environment, run the following lines of code
install.packages("renv") #install the package on the new computer (may not be necessary if renv bootstraps itself as expected)
renv::restore() #reinstall all the package versions in the renv lockfileLoad packages
packages <- c("genefilter", "DESeq2", "apeglm", "ashr", "ggplot2", "vsn",
"hexbin", "pheatmap", "RColorBrewer", "EnhancedVolcano", "tidyverse")
lapply(packages, library, character.only = TRUE)## Warning: package 'DESeq2' was built under R version 4.3.3## Warning: package 'GenomeInfoDb' was built under R version 4.3.3## Warning: package 'matrixStats' was built under R version 4.3.3## Warning: package 'hexbin' was built under R version 4.3.3## Warning: package 'ggrepel' was built under R version 4.3.3## [[1]]
## [1] "genefilter" "stats" "graphics" "grDevices" "datasets"
## [6] "utils" "methods" "base"
##
## [[2]]
## [1] "DESeq2" "SummarizedExperiment" "Biobase"
## [4] "MatrixGenerics" "matrixStats" "GenomicRanges"
## [7] "GenomeInfoDb" "IRanges" "S4Vectors"
## [10] "BiocGenerics" "stats4" "genefilter"
## [13] "stats" "graphics" "grDevices"
## [16] "datasets" "utils" "methods"
## [19] "base"
##
## [[3]]
## [1] "apeglm" "DESeq2" "SummarizedExperiment"
## [4] "Biobase" "MatrixGenerics" "matrixStats"
## [7] "GenomicRanges" "GenomeInfoDb" "IRanges"
## [10] "S4Vectors" "BiocGenerics" "stats4"
## [13] "genefilter" "stats" "graphics"
## [16] "grDevices" "datasets" "utils"
## [19] "methods" "base"
##
## [[4]]
## [1] "ashr" "apeglm" "DESeq2"
## [4] "SummarizedExperiment" "Biobase" "MatrixGenerics"
## [7] "matrixStats" "GenomicRanges" "GenomeInfoDb"
## [10] "IRanges" "S4Vectors" "BiocGenerics"
## [13] "stats4" "genefilter" "stats"
## [16] "graphics" "grDevices" "datasets"
## [19] "utils" "methods" "base"
##
## [[5]]
## [1] "ggplot2" "ashr" "apeglm"
## [4] "DESeq2" "SummarizedExperiment" "Biobase"
## [7] "MatrixGenerics" "matrixStats" "GenomicRanges"
## [10] "GenomeInfoDb" "IRanges" "S4Vectors"
## [13] "BiocGenerics" "stats4" "genefilter"
## [16] "stats" "graphics" "grDevices"
## [19] "datasets" "utils" "methods"
## [22] "base"
##
## [[6]]
## [1] "vsn" "ggplot2" "ashr"
## [4] "apeglm" "DESeq2" "SummarizedExperiment"
## [7] "Biobase" "MatrixGenerics" "matrixStats"
## [10] "GenomicRanges" "GenomeInfoDb" "IRanges"
## [13] "S4Vectors" "BiocGenerics" "stats4"
## [16] "genefilter" "stats" "graphics"
## [19] "grDevices" "datasets" "utils"
## [22] "methods" "base"
##
## [[7]]
## [1] "hexbin" "vsn" "ggplot2"
## [4] "ashr" "apeglm" "DESeq2"
## [7] "SummarizedExperiment" "Biobase" "MatrixGenerics"
## [10] "matrixStats" "GenomicRanges" "GenomeInfoDb"
## [13] "IRanges" "S4Vectors" "BiocGenerics"
## [16] "stats4" "genefilter" "stats"
## [19] "graphics" "grDevices" "datasets"
## [22] "utils" "methods" "base"
##
## [[8]]
## [1] "pheatmap" "hexbin" "vsn"
## [4] "ggplot2" "ashr" "apeglm"
## [7] "DESeq2" "SummarizedExperiment" "Biobase"
## [10] "MatrixGenerics" "matrixStats" "GenomicRanges"
## [13] "GenomeInfoDb" "IRanges" "S4Vectors"
## [16] "BiocGenerics" "stats4" "genefilter"
## [19] "stats" "graphics" "grDevices"
## [22] "datasets" "utils" "methods"
## [25] "base"
##
## [[9]]
## [1] "RColorBrewer" "pheatmap" "hexbin"
## [4] "vsn" "ggplot2" "ashr"
## [7] "apeglm" "DESeq2" "SummarizedExperiment"
## [10] "Biobase" "MatrixGenerics" "matrixStats"
## [13] "GenomicRanges" "GenomeInfoDb" "IRanges"
## [16] "S4Vectors" "BiocGenerics" "stats4"
## [19] "genefilter" "stats" "graphics"
## [22] "grDevices" "datasets" "utils"
## [25] "methods" "base"
##
## [[10]]
## [1] "EnhancedVolcano" "ggrepel" "RColorBrewer"
## [4] "pheatmap" "hexbin" "vsn"
## [7] "ggplot2" "ashr" "apeglm"
## [10] "DESeq2" "SummarizedExperiment" "Biobase"
## [13] "MatrixGenerics" "matrixStats" "GenomicRanges"
## [16] "GenomeInfoDb" "IRanges" "S4Vectors"
## [19] "BiocGenerics" "stats4" "genefilter"
## [22] "stats" "graphics" "grDevices"
## [25] "datasets" "utils" "methods"
## [28] "base"
##
## [[11]]
## [1] "lubridate" "forcats" "stringr"
## [4] "dplyr" "purrr" "readr"
## [7] "tidyr" "tibble" "tidyverse"
## [10] "EnhancedVolcano" "ggrepel" "RColorBrewer"
## [13] "pheatmap" "hexbin" "vsn"
## [16] "ggplot2" "ashr" "apeglm"
## [19] "DESeq2" "SummarizedExperiment" "Biobase"
## [22] "MatrixGenerics" "matrixStats" "GenomicRanges"
## [25] "GenomeInfoDb" "IRanges" "S4Vectors"
## [28] "BiocGenerics" "stats4" "genefilter"
## [31] "stats" "graphics" "grDevices"
## [34] "datasets" "utils" "methods"
## [37] "base"sessionInfo() #provides list of loaded packages and version of R.## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices datasets utils methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.3 forcats_1.0.0
## [3] stringr_1.5.1 dplyr_1.1.4
## [5] purrr_1.0.2 readr_2.1.5
## [7] tidyr_1.3.1 tibble_3.2.1
## [9] tidyverse_2.0.0 EnhancedVolcano_1.20.0
## [11] ggrepel_0.9.6 RColorBrewer_1.1-3
## [13] pheatmap_1.0.12 hexbin_1.28.4
## [15] vsn_3.70.0 ggplot2_3.5.1
## [17] ashr_2.2-63 apeglm_1.24.0
## [19] DESeq2_1.42.1 SummarizedExperiment_1.32.0
## [21] Biobase_2.62.0 MatrixGenerics_1.14.0
## [23] matrixStats_1.4.1 GenomicRanges_1.54.1
## [25] GenomeInfoDb_1.38.8 IRanges_2.36.0
## [27] S4Vectors_0.40.2 BiocGenerics_0.48.1
## [29] genefilter_1.84.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9 rlang_1.1.4
## [4] magrittr_2.0.3 compiler_4.3.2 RSQLite_2.3.7
## [7] png_0.1-8 vctrs_0.6.5 pkgconfig_2.0.3
## [10] crayon_1.5.3 fastmap_1.2.0 XVector_0.42.0
## [13] utf8_1.2.4 rmarkdown_2.28 tzdb_0.4.0
## [16] preprocessCore_1.64.0 bit_4.5.0 xfun_0.48
## [19] zlibbioc_1.48.2 cachem_1.1.0 jsonlite_1.8.9
## [22] blob_1.2.4 DelayedArray_0.28.0 BiocParallel_1.36.0
## [25] irlba_2.3.5.1 parallel_4.3.2 R6_2.5.1
## [28] stringi_1.8.4 bslib_0.8.0 limma_3.58.1
## [31] SQUAREM_2021.1 jquerylib_0.1.4 numDeriv_2016.8-1.1
## [34] Rcpp_1.0.13 knitr_1.48 timechange_0.3.0
## [37] Matrix_1.6-5 splines_4.3.2 tidyselect_1.2.1
## [40] rstudioapi_0.17.0 abind_1.4-8 yaml_2.3.10
## [43] codetools_0.2-20 affy_1.80.0 lattice_0.22-6
## [46] plyr_1.8.9 withr_3.0.1 KEGGREST_1.42.0
## [49] coda_0.19-4.1 evaluate_1.0.1 survival_3.7-0
## [52] Biostrings_2.70.3 pillar_1.9.0 affyio_1.72.0
## [55] BiocManager_1.30.25 renv_1.0.11 generics_0.1.3
## [58] invgamma_1.1 RCurl_1.98-1.16 truncnorm_1.0-9
## [61] emdbook_1.3.13 hms_1.1.3 munsell_0.5.1
## [64] scales_1.3.0 xtable_1.8-4 glue_1.8.0
## [67] tools_4.3.2 annotate_1.80.0 locfit_1.5-9.10
## [70] mvtnorm_1.3-1 XML_3.99-0.17 grid_4.3.2
## [73] bbmle_1.0.25.1 bdsmatrix_1.3-7 AnnotationDbi_1.64.1
## [76] colorspace_2.1-1 GenomeInfoDbData_1.2.11 cli_3.6.3
## [79] fansi_1.0.6 mixsqp_0.3-54 S4Arrays_1.2.1
## [82] gtable_0.3.5 sass_0.4.9 digest_0.6.37
## [85] SparseArray_1.2.4 memoise_2.0.1 htmltools_0.5.8.1
## [88] lifecycle_1.0.4 httr_1.4.7 statmod_1.5.0
## [91] bit64_4.5.2 MASS_7.3-60.0.1save_ggplot <- function(plot, filename, width = 10, height = 7, units = "in", dpi = 300) {
# Display plot
print(plot)
# Save plot
ggsave(filename = paste0(filename, ".png"), plot = plot, width = width, height = height, units = units, dpi = dpi)
}Read and clean count matrix and metadata
Read in raw count data
counts_raw <- read.csv("../output_RNA/stringtie-GeneExt/LCM_RNA_gene_count_matrix.csv", row.names = 1) #load in datagene_id,LCM_15,LCM_16,LCM_20,LCM_21,LCM_26,LCM_27,LCM_4,LCM_5,LCM_8,LCM_9
Read in metadata
meta <- read.csv("../data_RNA/LCM_RNA_metadata.csv") %>%
dplyr::arrange(Sample) %>%
mutate(across(c(Tissue, Fragment, Section_Date, LCM_Date), factor)) # Set variables as factors
meta$Tissue <- factor(meta$Tissue, levels = c("OralEpi","Aboral")) #we want OralEpi to be the baselineData sanity checks!
all(meta$Sample %in% colnames(counts_raw)) #are all of the sample names in the metadata column names in the gene count matrix? Should be TRUE## [1] TRUEall(meta$Sample == colnames(counts_raw)) #are they the same in the same order? Should be TRUE## [1] TRUEpOverA filtering to reduce dataset
ffun<-filterfun(pOverA(0.5,10)) #Keep genes expressed in at least 50% of samples -
counts_filt_poa <- genefilter((counts_raw), ffun) #apply filter
filtered_counts <- counts_raw[counts_filt_poa,] #keep only rows that passed filter
cat("Number of genes after filtering:", sum(counts_filt_poa))## Number of genes after filtering: 14464write.csv(filtered_counts, "../output_RNA/differential_expression/filtered_counts.csv")There are now 14464 genes in the filtered dataset.
Data sanity checks:
all(meta$Sample %in% colnames(filtered_counts)) #are all of the sample names in the metadata column names in the gene count matrix?## [1] TRUEall(meta$Sample == colnames(filtered_counts)) #are they the same in the same order? Should be TRUE## [1] TRUEDESeq2
Create DESeq object and run DESeq2
dds <- DESeqDataSetFromMatrix(countData = filtered_counts,
colData = meta,
design= ~ Fragment + Tissue)
dds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingExtract results for Aboral vs. OralEpi contrast
res <- results(dds, contrast = c("Tissue","Aboral","OralEpi"))
resLFC <- lfcShrink(dds, coef="Tissue_Aboral_vs_OralEpi", res=res, type = "apeglm")## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
## Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
## sequence count data: removing the noise and preserving large differences.
## Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895Extract results for adjusted p-value < 0.05
res <- resLFC
resOrdered <- res[order(res$pvalue),]# save differentially expressed genes
DE_05 <- as.data.frame(resOrdered) %>% filter(padj < 0.05)
DE_05_Up <- DE_05 %>% filter(log2FoldChange > 0) #Higher in Aboral, Lower in OralEpi
DE_05_Down <- DE_05 %>% filter(log2FoldChange < 0) #Lower in Aboral, Higher in OralEpi
nrow(DE_05)## [1] 3606nrow(DE_05_Up) #Higher in Aboral, Lower in OralEpi## [1] 804nrow(DE_05_Down) #Lower in Aboral, Higher in OralEpi## [1] 2802write.csv(as.data.frame(resOrdered),
file="../output_RNA/differential_expression/DESeq_results.csv")
write.csv(DE_05,
file="../output_RNA/differential_expression/DEG_05.csv")Visualizing Differential Expression
EnhancedVolcano(resLFC,
lab = rownames(resLFC),
x = 'log2FoldChange',
y = 'pvalue')
Plots
plotMA(results(dds, contrast = c("Tissue","Aboral","OralEpi")), ylim=c(-20,20))
plotMA(resLFC, ylim=c(-20,20))
Log2 Fold Change Comparison
# because we are interested in the comparison and not the intercept, we set 'coef=2'
resNorm <- lfcShrink(dds, coef=6, type="normal")## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
##
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895resAsh <- lfcShrink(dds, coef=6, type="ashr")## using 'ashr' for LFC shrinkage. If used in published research, please cite:
## Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
## https://doi.org/10.1093/biostatistics/kxw041par(mfrow=c(1,3), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-20,20)
plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm")
plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal")
plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr")
plotCounts(dds, gene=which.max(res$log2FoldChange), intgroup="Tissue")
plotCounts(dds, gene=which.min(res$log2FoldChange), intgroup="Tissue")
Transforming count data for visualization
vsd <- vst(dds, blind=FALSE)
rld <- rlog(dds, blind=FALSE)
ntd <- normTransform(dds) # this gives log2(n + 1)
meanSdPlot(assay(vsd), main = "vsd")## Warning in geom_hex(bins = bins, ...): Ignoring unknown parameters: `main`
meanSdPlot(assay(rld))
meanSdPlot(assay(ntd))
Will move forward with vst transformation for visualizations
Heatmap of count matrix
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
#view all genes
pheatmap(assay(vsd), cluster_rows=TRUE, show_rownames=FALSE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=df)
#view highest count genes
select <- order(rowMeans(counts(dds,normalized=TRUE)),
decreasing=TRUE)[1:20]
pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=df)
#view most significantly differentially expressed genes
select <- order(res$padj)[1:20]
pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=df)
Heatmap of the sample-to-sample distances
sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$Tissue, vsd$Fragment, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors)
Principal component plot of the samples
pcaData <- plotPCA(vsd, intgroup=c("Tissue", "Fragment"), returnData=TRUE)## using ntop=500 top features by variancepercentVar <- round(100 * attr(pcaData, "percentVar"))
PCA <- ggplot(pcaData, aes(PC1, PC2, color=Tissue, shape=Fragment)) +
geom_point(size=2) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
save_ggplot(PCA, "../output_RNA/differential_expression/PCA")
PCA_small <- ggplot(pcaData, aes(PC1, PC2, color=Tissue)) +
geom_point(size=2) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() + theme_bw()
ggsave(filename = paste0("../output_RNA/differential_expression/PCA_small", ".png"), plot = PCA_small, width = 4, height = 2.5, units = "in", dpi = 300)Clearly, the majority of the variance in the data is explained by tissue type!
Annotation data
Download annotation files from genome website
# wget files
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt.gz
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.EggNog_results.txt.gz
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.KEGG_results.txt.gz
# move to references direcotry
mv *gz ../references
# unzip files
gunzip ../references/*gzEggNog <- read.delim("../references/Pocillopora_acuta_HIv2.genes.EggNog_results.txt") %>% dplyr::rename("query" = X.query)
CDSearch <- read.delim("../references/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt", quote = "") %>% dplyr::rename("query" = X.Query)
KEGG <- read.delim("../references/Pocillopora_acuta_HIv2.genes.KEGG_results.txt", header = FALSE) %>% dplyr::rename("query" = V1, "KeggTerm" = V2)DE_05$query <- rownames(DE_05)
DE_05_annot <- DE_05 %>% left_join(CDSearch) %>% select(query,everything())## Joining with `by = join_by(query)`DE_05_eggnog <- DE_05 %>% left_join(EggNog) %>% select(query,everything())## Joining with `by = join_by(query)`annot_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(CDSearch)## Joining with `by = join_by(query)`eggnog_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(EggNog)## Joining with `by = join_by(query)`write.csv(as.data.frame(DE_05_eggnog),
file="../output_RNA/differential_expression/DE_05_eggnog_annotation.csv")df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
gene_labels <- eggnog_all %>% select(query,PFAMs) %>%
mutate_all(~ ifelse(is.na(.), "", .)) %>% #replace NAs with "" for labelling purposes
separate(PFAMs, into = c("PFAMs", "rest of name"), sep = ",(?=.*?,)", extra = "merge")## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 12723 rows [1, 2, 3, 4,
## 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 19, 20, 21, 23, 24, 25, ...].#view most significantly differentially expressed genes
select <- order(res$padj)[1:50]
top50_DE <- pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE")
#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]
up_Aboral <- pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral")
#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]
up_OralEpi <- pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi")
Genes of Interest
MarkerGenes <- read.csv("../references/Pacuta_MarkerGenes_Levy2021.csv") %>% dplyr::rename("query" = 1, "List" = 2, "definition" = 3)
MarkerGenes$def_short <- ifelse(nchar(MarkerGenes$definition) > 20,
paste0(substr(MarkerGenes$definition, 1, 17), "..."),
MarkerGenes$definition)Biomin <- read.csv("../references/Pacuta_Biomin.csv") %>% dplyr::rename("query" = Pocillopora_acuta_best_hit) %>% select(-c(accessionnumber.geneID, Ref))
Biomin <- Biomin %>%
group_by(query,List) %>%
summarize(definition = paste(unique(definition), collapse = ","))## `summarise()` has grouped output by 'query'. You can override using the
## `.groups` argument.Biomin$def_short <- ifelse(nchar(Biomin$definition) > 40,
paste0(substr(Biomin$definition, 1, 37), "..."),
Biomin$definition)DE_05$query <- rownames(DE_05)
DE_05_biomin <- DE_05 %>% left_join(Biomin) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`DE_05_marker <- DE_05 %>% left_join(MarkerGenes) %>% select(query,everything()) %>% drop_na()## Joining with `by = join_by(query)`write.csv(as.data.frame(DE_05_marker),
file="../output_RNA/differential_expression/DE_05_markergene_annotation.csv")
biomin_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(Biomin) ## Joining with `by = join_by(query)`biomin_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(Biomin) ## Joining with `by = join_by(query)`markers_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(MarkerGenes) ## Joining with `by = join_by(query)`markers_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(MarkerGenes) ## Joining with `by = join_by(query)`df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
#view biomin genes that are differntially expressed
DE_biomin <- pheatmap(assay(vsd)[DE_05_biomin$query,], cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
labels_row = DE_05_biomin$def_short, fontsize_row = 5)
DE_biomin
save_ggplot(DE_biomin, "../output_RNA/differential_expression/DE_biomin")
#view marker genes that are differntially expressed
DE_marker <- pheatmap(assay(vsd)[DE_05_marker$query,], cluster_rows=TRUE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,cutree_rows = 5,annotation_col=df,
labels_row = DE_05_marker$List, fontsize_row = 4)
DE_marker
save_ggplot(DE_marker, "../output_RNA/differential_expression/DE_marker")
DE_05_marker_grouped <- DE_05_marker %>% arrange(List) %>% mutate(List = as.factor(List))
DE_05_marker_grouped_plot <- pheatmap(assay(vsd)[DE_05_marker_grouped$query,], cluster_rows=FALSE, show_rownames=TRUE,
cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
labels_row = DE_05_marker_grouped$List, fontsize_row = 5)
DE_05_marker_grouped_plot
save_ggplot(DE_05_marker_grouped_plot, "../output_RNA/differential_expression/DE_05_marker_grouped")
Visualizing Differential Expression
Biomin_volcano <- EnhancedVolcano(biomin_all_res,
lab = biomin_all_res$def_short,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 40)
save_ggplot(Biomin_volcano, "../output_RNA/differential_expression/Biomin_volcano")
Marker_volcano <- EnhancedVolcano(markers_all_res,
lab = markers_all_res$List,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(Marker_volcano, "../output_RNA/differential_expression/Marker_volcano")## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Marker_volcano_names <- EnhancedVolcano(markers_all_res,
lab = markers_all_res$def_short,
x = 'log2FoldChange',
y = 'padj',
pCutoff = 0.01,
drawConnectors = TRUE,
widthConnectors = 0.75,
pointSize = 1,
labSize = 2,boxedLabels = TRUE,max.overlaps = 60)
save_ggplot(Marker_volcano_names, "../output_RNA/differential_expression/Marker_volcano_names")
we probably want to correct for batch effects (LCM date) + genotype (Fragment)
and to decide which tranformation to use
Updating Renv environment:
After you’ve confirmed your code works as expected, use renv::snapshot() to record the packages and their sources in the lockfile.”